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    "# Async callbacks\n",
    "\n",
    "If you are planning to use the async API, it is recommended to use `AsyncCallbackHandler` to avoid blocking the runloop. \n",
    "\n",
    "**Advanced** if you use a sync `CallbackHandler` while using an async method to run your LLM / Chain / Tool / Agent, it will still work. However, under the hood, it will be called with [`run_in_executor`](https://docs.python.org/3/library/asyncio-eventloop.html#asyncio.loop.run_in_executor) which can cause issues if your `CallbackHandler` is not thread-safe."
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     "text": [
      "zzzz....\n",
      "Hi! I just woke up. Your llm is starting\n",
      "Sync handler being called in a `thread_pool_executor`: token: \n",
      "Sync handler being called in a `thread_pool_executor`: token: Why\n",
      "Sync handler being called in a `thread_pool_executor`: token:  don\n",
      "Sync handler being called in a `thread_pool_executor`: token: 't\n",
      "Sync handler being called in a `thread_pool_executor`: token:  scientists\n",
      "Sync handler being called in a `thread_pool_executor`: token:  trust\n",
      "Sync handler being called in a `thread_pool_executor`: token:  atoms\n",
      "Sync handler being called in a `thread_pool_executor`: token: ?\n",
      "Sync handler being called in a `thread_pool_executor`: token:  \n",
      "\n",
      "\n",
      "Sync handler being called in a `thread_pool_executor`: token: Because\n",
      "Sync handler being called in a `thread_pool_executor`: token:  they\n",
      "Sync handler being called in a `thread_pool_executor`: token:  make\n",
      "Sync handler being called in a `thread_pool_executor`: token:  up\n",
      "Sync handler being called in a `thread_pool_executor`: token:  everything\n",
      "Sync handler being called in a `thread_pool_executor`: token: .\n",
      "Sync handler being called in a `thread_pool_executor`: token: \n",
      "zzzz....\n",
      "Hi! I just woke up. Your llm is ending\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LLMResult(generations=[[ChatGeneration(text=\"Why don't scientists trust atoms? \\n\\nBecause they make up everything.\", generation_info=None, message=AIMessage(content=\"Why don't scientists trust atoms? \\n\\nBecause they make up everything.\", additional_kwargs={}, example=False))]], llm_output={'token_usage': {}, 'model_name': 'gpt-3.5-turbo'})"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import asyncio\n",
    "from typing import Any, Dict, List\n",
    "\n",
    "from langchain.chat_models import ChatOpenAI\n",
    "from langchain.schema import LLMResult, HumanMessage\n",
    "from langchain.callbacks.base import AsyncCallbackHandler, BaseCallbackHandler\n",
    "\n",
    "\n",
    "class MyCustomSyncHandler(BaseCallbackHandler):\n",
    "    def on_llm_new_token(self, token: str, **kwargs) -> None:\n",
    "        print(f\"Sync handler being called in a `thread_pool_executor`: token: {token}\")\n",
    "\n",
    "\n",
    "class MyCustomAsyncHandler(AsyncCallbackHandler):\n",
    "    \"\"\"Async callback handler that can be used to handle callbacks from langchain.\"\"\"\n",
    "\n",
    "    async def on_llm_start(\n",
    "        self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any\n",
    "    ) -> None:\n",
    "        \"\"\"Run when chain starts running.\"\"\"\n",
    "        print(\"zzzz....\")\n",
    "        await asyncio.sleep(0.3)\n",
    "        class_name = serialized[\"name\"]\n",
    "        print(\"Hi! I just woke up. Your llm is starting\")\n",
    "\n",
    "    async def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:\n",
    "        \"\"\"Run when chain ends running.\"\"\"\n",
    "        print(\"zzzz....\")\n",
    "        await asyncio.sleep(0.3)\n",
    "        print(\"Hi! I just woke up. Your llm is ending\")\n",
    "\n",
    "\n",
    "# To enable streaming, we pass in `streaming=True` to the ChatModel constructor\n",
    "# Additionally, we pass in a list with our custom handler\n",
    "chat = ChatOpenAI(\n",
    "    max_tokens=25,\n",
    "    streaming=True,\n",
    "    callbacks=[MyCustomSyncHandler(), MyCustomAsyncHandler()],\n",
    ")\n",
    "\n",
    "await chat.agenerate([[HumanMessage(content=\"Tell me a joke\")]])"
   ]
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